論文

査読有り
2020年12月

Massively parallel causal inference of whole brain dynamics at single neuron resolution

Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
  • Wassapon Watanakeesuntorn
  • ,
  • Keichi Takahashi
  • ,
  • Kohei Ichikawa
  • ,
  • Joseph Park
  • ,
  • George Sugihara
  • ,
  • Ryousei Takano
  • ,
  • Jason Haga
  • ,
  • Gerald M. Pao

2020-December
開始ページ
196
終了ページ
205
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ICPADS51040.2020.00035

Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to computational cost. With the growth of data collection capabilities, there is a great need to identify causal relationships in large datasets. We present mpEDM, a parallel distributed implementation of EDM optimized for modern GPU-centric supercomputers. We improve the original algorithm to reduce redundant computation and optimize the implementation to fully utilize hardware resources such as GPUs and SIMD units. As a use case, we run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an entire animal brain sampled at single neuron resolution to identify dynamical causation patterns across the brain. mpEDM is 1, 530× faster than cppEDM and a dataset containing 101, 729 neuron was analyzed in 199 seconds on 512 nodes. This is the largest EDM causal inference achieved to date.

リンク情報
DOI
https://doi.org/10.1109/ICPADS51040.2020.00035
arXiv
http://arxiv.org/abs/arXiv:2011.11082
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102347295&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85102347295&origin=inward
URL
https://arxiv.org/abs/2011.11082
URL
https://dblp.uni-trier.de/db/journals/corr/corr2011.html#abs-2011-11082

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